
"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
					import tensorflow as tf

if is_keras_nlp_available():
					from transformers.models.gpta import TFGPTaTokenizer


UpperCAmelCase__ :       Union[str, Any]       				=					['gpt2']
UpperCAmelCase__ :       Optional[int]       				=					'gpt2'

if is_tf_available():



					class       lowerCAmelCase_		(tf.Module    ):


									"""simple docstring"""



									def __init__(self  ,			SCREAMING_SNAKE_CASE__       )   ->						str:



																"""simple docstring"""




																super().__init__()
																SCREAMING_SNAKE_CASE__		:			List[str]	 =  tokenizer
																SCREAMING_SNAKE_CASE__		:			Tuple	 =  AutoConfig.from_pretrained(_A       )
																SCREAMING_SNAKE_CASE__		:			Dict	 =  TFGPTaLMHeadModel.from_config(_A       )



									@tf.function(input_signature=(tf.TensorSpec((None,)  ,			tf.string  ,			name="""text"""       ),)       )
									def       __magic_name__       (self  ,			SCREAMING_SNAKE_CASE__       )   ->						Union[str, Any]:



																"""simple docstring"""




																SCREAMING_SNAKE_CASE__		:			Optional[Any]	 =  self.tokenizer(_A       )
																SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  tokenized['''input_ids'''].to_tensor()

																SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  tf.cast(input_ids_dense > 0  ,			tf.intaa       )
																# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																SCREAMING_SNAKE_CASE__		:			Any	 =  self.model(input_ids=_A  ,			attention_mask=_A       )['''logits''']

																return outputs



@require_tf
@require_keras_nlp
class       lowerCAmelCase_		(unittest.TestCase    ):


				"""simple docstring"""



				def       __magic_name__       (self       )   ->						List[str]:



											"""simple docstring"""




											super().setUp()

											SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  [GPTaTokenizer.from_pretrained(_A       ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
											SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  [TFGPTaTokenizer.from_pretrained(_A       ) for checkpoint in TOKENIZER_CHECKPOINTS]
											assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

											SCREAMING_SNAKE_CASE__		:			str	 =  [
											    '''This is a straightforward English test sentence.''',
											    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
											    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
											    '''And some much more rare Chinese: 齉 堃 齉堃''',
											    '''Je vais aussi écrire en français pour tester les accents''',
											    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
											]
											SCREAMING_SNAKE_CASE__		:			int	 =  list(zip(self.test_sentences  ,			self.test_sentences[::-1]       )       )



				def       __magic_name__       (self       )   ->						Dict:



											"""simple docstring"""




											for tokenizer, tf_tokenizer in zip(self.tokenizers  ,			self.tf_tokenizers       ):
																		for test_inputs in self.test_sentences:
																									SCREAMING_SNAKE_CASE__		:			int	 =  tokenizer([test_inputs]  ,			return_tensors="""tf"""       )
																									SCREAMING_SNAKE_CASE__		:			int	 =  tf_tokenizer([test_inputs]       )

																									for key in python_outputs.keys():
																																# convert them to numpy to avoid messing with ragged tensors
																																SCREAMING_SNAKE_CASE__		:			int	 =  python_outputs[key].numpy()
																																SCREAMING_SNAKE_CASE__		:			Dict	 =  tf_outputs[key].numpy()

																																self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape       )       )
																																self.assertTrue(tf.reduce_all(tf.cast(_A  ,			tf.intaa       ) == tf_outputs_values       )       )



				@slow
				def       __magic_name__       (self       )   ->						Optional[int]:



											"""simple docstring"""




											for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE__		:			Dict	 =  tf.function(_A       )
																		for test_inputs in self.test_sentences:
																									SCREAMING_SNAKE_CASE__		:			str	 =  tf.constant(_A       )
																									SCREAMING_SNAKE_CASE__		:			Tuple	 =  compiled_tokenizer(_A       )
																									SCREAMING_SNAKE_CASE__		:			List[Any]	 =  tf_tokenizer(_A       )

																									for key in eager_outputs.keys():
																																self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )



				@slow
				def       __magic_name__       (self       )   ->						Optional[int]:



											"""simple docstring"""




											for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE__		:			List[Any]	 =  ModelToSave(tokenizer=_A       )
																		SCREAMING_SNAKE_CASE__		:			List[Any]	 =  tf.convert_to_tensor([self.test_sentences[0]]       )
																		SCREAMING_SNAKE_CASE__		:			Dict	 =  model.serving(_A       )  # Build model with some sample inputs
																		with TemporaryDirectory() as tempdir:
																									SCREAMING_SNAKE_CASE__		:			Tuple	 =  Path(_A       ) / '''saved.model'''
																									tf.saved_model.save(_A  ,			_A  ,			signatures={"""serving_default""": model.serving}       )
																									SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  tf.saved_model.load(_A       )
																		SCREAMING_SNAKE_CASE__		:			int	 =  loaded_model.signatures['''serving_default'''](_A       )['''output_0''']
																		# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																		self.assertTrue(tf.reduce_all(out == loaded_output       )       )



				@slow
				def       __magic_name__       (self       )   ->						Tuple:



											"""simple docstring"""




											for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  tf.convert_to_tensor([self.test_sentences[0]]       )
																		SCREAMING_SNAKE_CASE__		:			List[str]	 =  tf_tokenizer(_A       )  # Build model with some sample inputs

																		SCREAMING_SNAKE_CASE__		:			List[Any]	 =  tf_tokenizer.get_config()
																		SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  TFGPTaTokenizer.from_config(_A       )
																		SCREAMING_SNAKE_CASE__		:			Any	 =  model_from_config(_A       )

																		for key in from_config_output.keys():
																									self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]       )       )



				@slow
				def       __magic_name__       (self       )   ->						List[Any]:



											"""simple docstring"""




											for tf_tokenizer in self.tf_tokenizers:
																		# for the test to run
																		SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  12_31_23

																		for max_length in [3, 5, 10_24]:
																									SCREAMING_SNAKE_CASE__		:			Union[str, Any]	 =  tf.convert_to_tensor([self.test_sentences[0]]       )
																									SCREAMING_SNAKE_CASE__		:			Dict	 =  tf_tokenizer(_A  ,			max_length=_A       )

																									SCREAMING_SNAKE_CASE__		:			Optional[int]	 =  out['''input_ids'''].numpy().shape[1]

																									assert out_length == max_length












'''simple docstring'''





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_UpperCamelCase						:						str  =					datasets.utils.logging.get_logger(__name__)


@dataclass
class  snake_case__      (					datasets.BuilderConfig):
					a_        =					None
					a_        =					"utf-8"
					a_        =					None
					a_        =					None
					a_        =					True  # deprecated
					a_        =					None  # deprecated
					a_        =					10 << 20  # 10MB
					a_        =					None






class  snake_case__      (					datasets.ArrowBasedBuilder):
					a_        =					JsonConfig




					def 		A      (  self  :		Union[str, Any]      )    ->							Optional[int]:
								if self.config.block_size is not None:
											logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''      )
											UpperCAmelCase_      :       Union[str, Any]          =       self.config.block_size
								if self.config.use_threads is not True:
											logger.warning(
											    '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''      )
								if self.config.newlines_in_values is not None:
											raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''      )
								return datasets.DatasetInfo(features=self.config.features      )




					def 		A      (  self  :		Optional[int]				, _A  :		List[str]      )    ->							int:
								if not self.config.data_files:
											raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"      )
								UpperCAmelCase_      :       Optional[Any]          =       dl_manager.download_and_extract(self.config.data_files      )
								if isinstance(_A				, (str, list, tuple)      ):
											UpperCAmelCase_      :       List[Any]          =       data_files
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       List[str]          =       [files]
											UpperCAmelCase_      :       List[str]          =       [dl_manager.iter_files(_A      ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN				, gen_kwargs={'''files''': files}      )]
								UpperCAmelCase_      :       Tuple          =       []
								for split_name, files in data_files.items():
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       Optional[Any]          =       [files]
											UpperCAmelCase_      :       List[Any]          =       [dl_manager.iter_files(_A      ) for file in files]
											splits.append(datasets.SplitGenerator(name=_A				, gen_kwargs={'''files''': files}      )      )
								return splits




					def 		A      (  self  :		List[Any]				, _A  :		pa.Table      )    ->							pa.Table:
								if self.config.features is not None:
											# adding missing columns
											for column_name in set(self.config.features      ) - set(pa_table.column_names      ):
														UpperCAmelCase_      :       Union[str, Any]          =       self.config.features.arrow_schema.field(_A      ).type
														UpperCAmelCase_      :       int          =       pa_table.append_column(_A				, pa.array([None] * len(_A      )				, type=_A      )      )
											# more expensive cast to support nested structures with keys in a different order
											# allows str <-> int/float or str to Audio for example
											UpperCAmelCase_      :       Dict          =       table_cast(_A				, self.config.features.arrow_schema      )
								return pa_table





					def 		A      (  self  :		Optional[Any]				, _A  :		List[Any]      )    ->							Optional[int]:
								for file_idx, file in enumerate(itertools.chain.from_iterable(_A      )      ):
											# If the file is one json object and if we need to look at the list of items in one specific field
											if self.config.field is not None:
														with open(_A				, encoding=self.config.encoding				, errors=self.config.encoding_errors      ) as f:
																	UpperCAmelCase_      :       Optional[int]          =       json.load(_A      )

														# We keep only the field we are interested in
														UpperCAmelCase_      :       Any          =       dataset[self.config.field]

														# We accept two format: a list of dicts or a dict of lists
														if isinstance(_A				, (list, tuple)      ):
																	UpperCAmelCase_      :       Optional[int]          =       set().union(*[row.keys() for row in dataset]      )
																	UpperCAmelCase_      :       str          =       {col: [row.get(_A      ) for row in dataset] for col in keys}
														else:
																	UpperCAmelCase_      :       Tuple          =       dataset
														UpperCAmelCase_      :       List[str]          =       pa.Table.from_pydict(_A      )
														yield file_idx, self._cast_table(_A      )

											# If the file has one json object per line
											else:
														with open(_A				, '''rb'''      ) as f:
																	UpperCAmelCase_      :       Tuple          =       0
																	# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																	# Set a default minimum value of 16kB if the chunk size is really small
																	UpperCAmelCase_      :       Optional[Any]          =       max(self.config.chunksize // 32				, 16 << 10      )
																	UpperCAmelCase_      :       Tuple          =       (
																	    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																	)
																	while True:
																				UpperCAmelCase_      :       Optional[Any]          =       f.read(self.config.chunksize      )
																				if not batch:
																							break
																				# Finish current line
																				try:
																							batch += f.readline()
																				except (AttributeError, io.UnsupportedOperation):
																							batch += readline(_A      )
																				# PyArrow only accepts utf-8 encoded bytes
																				if self.config.encoding != "utf-8":
																							UpperCAmelCase_      :       List[Any]          =       batch.decode(self.config.encoding				, errors=_A      ).encode('''utf-8'''      )
																				try:
																							while True:
																										try:
																													UpperCAmelCase_      :       int          =       paj.read_json(
																													    io.BytesIO(_A      )				, read_options=paj.ReadOptions(block_size=_A      )      )
																													break
																										except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																													if (
																													    isinstance(_A				, pa.ArrowInvalid      )
																													    and "straddling" not in str(_A      )
																													    or block_size > len(_A      )
																													):
																																raise
																													else:
																																# Increase the block size in case it was too small.
																																# The block size will be reset for the next file.
																																logger.debug(
																																    F"Batch of {len(_A      )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."      )
																																block_size *= 2
																				except pa.ArrowInvalid as e:
																							try:
																										with open(
																										    _A				, encoding=self.config.encoding				, errors=self.config.encoding_errors      ) as f:
																													UpperCAmelCase_      :       List[Any]          =       json.load(_A      )
																							except json.JSONDecodeError:
																										logger.error(F"Failed to read file '{file}' with error {type(_A      )}: {e}"      )
																										raise e
																							# If possible, parse the file as a list of json objects and exit the loop
																							if isinstance(_A				, _A      ):  # list is the only sequence type supported in JSON
																										try:
																													UpperCAmelCase_      :       Optional[int]          =       set().union(*[row.keys() for row in dataset]      )
																													UpperCAmelCase_      :       Any          =       {col: [row.get(_A      ) for row in dataset] for col in keys}
																													UpperCAmelCase_      :       Optional[int]          =       pa.Table.from_pydict(_A      )
																										except (pa.ArrowInvalid, AttributeError) as e:
																													logger.error(F"Failed to read file '{file}' with error {type(_A      )}: {e}"      )
																													raise ValueError(F"Not able to read records in the JSON file at {file}."      ) from None
																										yield file_idx, self._cast_table(_A      )
																										break
																							else:
																										logger.error(F"Failed to read file '{file}' with error {type(_A      )}: {e}"      )
																										raise ValueError(
																										    F"Not able to read records in the JSON file at {file}. "
																										    F"You should probably indicate the field of the JSON file containing your records. "
																										    F"This JSON file contain the following fields: {str(list(dataset.keys()      )      )}. "
																										    F"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "      ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																				yield (file_idx, batch_idx), self._cast_table(_A      )
																				batch_idx += 1


